Overview

Dataset statistics

Number of variables 17
Number of observations 169302
Missing cells 0
Missing cells (%) 0.0%
Duplicate rows 0
Duplicate rows (%) 0.0%
Total size in memory 22.0 MiB
Average record size in memory 136.0 B

Variable types

NUM 13
BOOL 2
CAT 2

Warnings

name has a high cardinality: 132939 distinct values High cardinality
artists has a high cardinality: 33375 distinct values High cardinality
name is uniformly distributed Uniform
df_index has unique values Unique
instrumentalness has 46055 (27.2%) zeros Zeros
key has 21431 (12.7%) zeros Zeros
popularity has 26802 (15.8%) zeros Zeros

Reproduction

Analysis started 2020-09-16 13:32:12.127976
Analysis finished 2020-09-16 13:32:52.102698
Duration 39.97 seconds
Software version pandas-profiling v2.9.0
Download configuration config.yaml

Variables

df_index
Real number (ℝ≥0)

UNIQUE

Distinct 169302
Distinct (%) 100.0%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean 84849.84659
Minimum 0
Maximum 169908
Zeros 1
Zeros (%) < 0.1%
Memory size 1.3 MiB
2020-09-16T21:32:52.426172 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum 0
5-th percentile 8474.05
Q1 42392.25
median 84813.5
Q3 127255.75
95-th percentile 161353.95
Maximum 169908
Range 169908
Interquartile range (IQR) 84863.5

Descriptive statistics

Standard deviation 49030.07791
Coefficient of variation (CV) 0.5778452157
Kurtosis -1.198528767
Mean 84849.84659
Median Absolute Deviation (MAD) 42432
Skewness 0.0024169362
Sum 1.436524873e+10
Variance 2403948540
Monotocity Strictly increasing
2020-09-16T21:32:52.665920 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)  
2047 1 < 0.1%
 
159079 1 < 0.1%
 
163173 1 < 0.1%
 
161124 1 < 0.1%
 
150883 1 < 0.1%
 
148834 1 < 0.1%
 
154977 1 < 0.1%
 
152928 1 < 0.1%
 
44351 1 < 0.1%
 
42302 1 < 0.1%
 
Other values (169292) 169292 > 99.9%
 
Value Count Frequency (%)  
0 1 < 0.1%
 
1 1 < 0.1%
 
2 1 < 0.1%
 
3 1 < 0.1%
 
4 1 < 0.1%
 
Value Count Frequency (%)  
169908 1 < 0.1%
 
169907 1 < 0.1%
 
169906 1 < 0.1%
 
169905 1 < 0.1%
 
169904 1 < 0.1%
 

name
Categorical

HIGH CARDINALITY
UNIFORM

Distinct 132939
Distinct (%) 78.5%
Missing 0
Missing (%) 0.0%
Memory size 1.3 MiB
Summertime
 
62
Overture
 
43
Home
 
40
Stay
 
34
You
 
33
Other values (132934)
169090 
Value Count Frequency (%)  
Summertime 62 < 0.1%
 
Overture 43 < 0.1%
 
Home 40 < 0.1%
 
Stay 34 < 0.1%
 
You 33 < 0.1%
 
I Love You 32 < 0.1%
 
Forever 32 < 0.1%
 
Angel 31 < 0.1%
 
Paradise 31 < 0.1%
 
Runaway 30 < 0.1%
 
Other values (132929) 168934 99.8%
 
2020-09-16T21:32:53.435539 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique 113871 ?
Unique (%) 67.3%
2020-09-16T21:32:53.616199 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length 255
Median length 18
Mean length 23.50569987
Min length 1

artists
Categorical

HIGH CARDINALITY

Distinct 33375
Distinct (%) 19.7%
Missing 0
Missing (%) 0.0%
Memory size 1.3 MiB
['Эрнест Хемингуэй']
 
1215
['Francisco Canaro']
 
938
['Эрих Мария Ремарк']
 
781
['Ignacio Corsini']
 
620
['Frank Sinatra']
 
592
Other values (33370)
165156 
Value Count Frequency (%)  
['Эрнест Хемингуэй'] 1215 0.7%
 
['Francisco Canaro'] 938 0.6%
 
['Эрих Мария Ремарк'] 781 0.5%
 
['Ignacio Corsini'] 620 0.4%
 
['Frank Sinatra'] 592 0.3%
 
['Bob Dylan'] 539 0.3%
 
['The Rolling Stones'] 512 0.3%
 
['Johnny Cash'] 501 0.3%
 
['The Beach Boys'] 491 0.3%
 
['Elvis Presley'] 488 0.3%
 
Other values (33365) 162625 96.1%
 
2020-09-16T21:32:53.899920 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
Frequencies of value counts

Unique

Unique 19627 ?
Unique (%) 11.6%
2020-09-16T21:32:54.057331 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
Histogram of lengths of the category

Length

Max length 661
Median length 17
Mean length 23.24452753
Min length 5

duration_ms
Real number (ℝ≥0)

Distinct 50212
Distinct (%) 29.7%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean 231435.0531
Minimum 5108
Maximum 5403500
Zeros 0
Zeros (%) 0.0%
Memory size 1.3 MiB
2020-09-16T21:32:54.210222 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum 5108
5-th percentile 112667
Q1 171187
median 208621
Q3 262907
95-th percentile 409640
Maximum 5403500
Range 5398392
Interquartile range (IQR) 91720

Descriptive statistics

Standard deviation 121133.4509
Coefficient of variation (CV) 0.523401487
Kurtosis 117.0778563
Mean 231435.0531
Median Absolute Deviation (MAD) 44088
Skewness 6.528090803
Sum 3.918241735e+10
Variance 1.467331293e+10
Monotocity Not monotonic
2020-09-16T21:32:54.353749 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)  
192000 55 < 0.1%
 
180000 53 < 0.1%
 
186000 50 < 0.1%
 
240000 50 < 0.1%
 
184000 49 < 0.1%
 
160000 47 < 0.1%
 
168000 45 < 0.1%
 
169000 45 < 0.1%
 
170000 44 < 0.1%
 
175000 44 < 0.1%
 
Other values (50202) 168820 99.7%
 
Value Count Frequency (%)  
5108 1 < 0.1%
 
5991 1 < 0.1%
 
6362 1 < 0.1%
 
6467 1 < 0.1%
 
8853 2 < 0.1%
 
Value Count Frequency (%)  
5403500 1 < 0.1%
 
4270034 1 < 0.1%
 
4269407 1 < 0.1%
 
4120258 2 < 0.1%
 
3816373 1 < 0.1%
 

year
Real number (ℝ≥0)

Distinct 100
Distinct (%) 0.1%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean 1977.330362
Minimum 1921
Maximum 2020
Zeros 0
Zeros (%) 0.0%
Memory size 1.3 MiB
2020-09-16T21:32:54.501954 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum 1921
5-th percentile 1935
Q1 1957
median 1978
Q3 1999
95-th percentile 2016
Maximum 2020
Range 99
Interquartile range (IQR) 42

Descriptive statistics

Standard deviation 25.54741476
Coefficient of variation (CV) 0.01292015499
Kurtosis -1.021059253
Mean 1977.330362
Median Absolute Deviation (MAD) 21
Skewness -0.1368376892
Sum 334765985
Variance 652.6704007
Monotocity Not monotonic
2020-09-16T21:32:54.656848 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)  
1993 2000 1.2%
 
1996 2000 1.2%
 
1994 2000 1.2%
 
1966 2000 1.2%
 
1992 2000 1.2%
 
1962 2000 1.2%
 
1990 2000 1.2%
 
1989 2000 1.2%
 
1988 2000 1.2%
 
1986 2000 1.2%
 
Other values (90) 149302 88.2%
 
Value Count Frequency (%)  
1921 128 0.1%
 
1922 72 < 0.1%
 
1923 169 0.1%
 
1924 237 0.1%
 
1925 263 0.2%
 
Value Count Frequency (%)  
2020 1741 1.0%
 
2019 2000 1.2%
 
2018 1999 1.2%
 
2017 1999 1.2%
 
2016 1969 1.2%
 

acousticness
Real number (ℝ≥0)

Distinct 4714
Distinct (%) 2.8%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean 0.4917566408
Minimum 0
Maximum 0.996
Zeros 21
Zeros (%) < 0.1%
Memory size 1.3 MiB
2020-09-16T21:32:54.810608 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum 0
5-th percentile 0.00143
Q1 0.093625
median 0.489
Q3 0.886
95-th percentile 0.992
Maximum 0.996
Range 0.996
Interquartile range (IQR) 0.792375

Descriptive statistics

Standard deviation 0.3762684712
Coefficient of variation (CV) 0.7651517844
Kurtosis -1.612390863
Mean 0.4917566408
Median Absolute Deviation (MAD) 0.396
Skewness 0.01405904966
Sum 83255.3828
Variance 0.1415779624
Monotocity Not monotonic
2020-09-16T21:32:54.949030 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)  
0.995 2971 1.8%
 
0.994 2245 1.3%
 
0.993 1697 1.0%
 
0.992 1466 0.9%
 
0.991 1239 0.7%
 
0.99 1156 0.7%
 
0.996 1031 0.6%
 
0.989 1022 0.6%
 
0.988 896 0.5%
 
0.987 800 0.5%
 
Other values (4704) 154779 91.4%
 
Value Count Frequency (%)  
0 21 < 0.1%
 
1e-06 1 < 0.1%
 
1.01e-06 3 < 0.1%
 
1.03e-06 1 < 0.1%
 
1.05e-06 2 < 0.1%
 
Value Count Frequency (%)  
0.996 1031 0.6%
 
0.995 2971 1.8%
 
0.994 2245 1.3%
 
0.993 1697 1.0%
 
0.992 1466 0.9%
 

danceability
Real number (ℝ≥0)

Distinct 1232
Distinct (%) 0.7%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean 0.5386953793
Minimum 0
Maximum 0.988
Zeros 146
Zeros (%) 0.1%
Memory size 1.3 MiB
2020-09-16T21:32:55.097276 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum 0
5-th percentile 0.233
Q1 0.418
median 0.549
Q3 0.668
95-th percentile 0.813
Maximum 0.988
Range 0.988
Interquartile range (IQR) 0.25

Descriptive statistics

Standard deviation 0.1751954374
Coefficient of variation (CV) 0.3252217192
Kurtosis -0.4202513441
Mean 0.5386953793
Median Absolute Deviation (MAD) 0.124
Skewness -0.2156799167
Sum 91202.2051
Variance 0.03069344128
Monotocity Not monotonic
2020-09-16T21:32:55.499994 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)  
0.565 436 0.3%
 
0.578 411 0.2%
 
0.612 410 0.2%
 
0.559 401 0.2%
 
0.556 399 0.2%
 
0.622 397 0.2%
 
0.61 397 0.2%
 
0.602 395 0.2%
 
0.546 391 0.2%
 
0.545 390 0.2%
 
Other values (1222) 165275 97.6%
 
Value Count Frequency (%)  
0 146 0.1%
 
0.0551 1 < 0.1%
 
0.0559 2 < 0.1%
 
0.0562 1 < 0.1%
 
0.0569 2 < 0.1%
 
Value Count Frequency (%)  
0.988 1 < 0.1%
 
0.986 2 < 0.1%
 
0.985 1 < 0.1%
 
0.982 1 < 0.1%
 
0.98 3 < 0.1%
 

energy
Real number (ℝ≥0)

Distinct 2332
Distinct (%) 1.4%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean 0.4895964759
Minimum 0
Maximum 1
Zeros 10
Zeros (%) < 0.1%
Memory size 1.3 MiB
2020-09-16T21:32:55.653248 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum 0
5-th percentile 0.077505
Q1 0.264
median 0.482
Q3 0.71075
95-th percentile 0.925
Maximum 1
Range 1
Interquartile range (IQR) 0.44675

Descriptive statistics

Standard deviation 0.267074182
Coefficient of variation (CV) 0.5454985791
Kurtosis -1.096355636
Mean 0.4895964759
Median Absolute Deviation (MAD) 0.223
Skewness 0.07442018773
Sum 82889.66256
Variance 0.07132861867
Monotocity Not monotonic
2020-09-16T21:32:55.798112 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)  
0.28 235 0.1%
 
0.459 234 0.1%
 
0.254 233 0.1%
 
0.341 232 0.1%
 
0.306 232 0.1%
 
0.299 231 0.1%
 
0.31 230 0.1%
 
0.32 230 0.1%
 
0.245 230 0.1%
 
0.219 227 0.1%
 
Other values (2322) 166988 98.6%
 
Value Count Frequency (%)  
0 10 < 0.1%
 
1.99e-05 2 < 0.1%
 
2.01e-05 6 < 0.1%
 
2.02e-05 5 < 0.1%
 
2.03e-05 14 < 0.1%
 
Value Count Frequency (%)  
1 20 < 0.1%
 
0.999 25 < 0.1%
 
0.998 38 < 0.1%
 
0.997 56 < 0.1%
 
0.996 67 < 0.1%
 

instrumentalness
Real number (ℝ≥0)

ZEROS

Distinct 5401
Distinct (%) 3.2%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean 0.1604745519
Minimum 0
Maximum 1
Zeros 46055
Zeros (%) 27.2%
Memory size 1.3 MiB
2020-09-16T21:32:55.944436 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum 0
5-th percentile 0
Q1 0
median 0.000197
Q3 0.083275
95-th percentile 0.903
Maximum 1
Range 1
Interquartile range (IQR) 0.083275

Descriptive statistics

Standard deviation 0.3079964242
Coefficient of variation (CV) 1.919285149
Kurtosis 1.171686173
Mean 0.1604745519
Median Absolute Deviation (MAD) 0.000197
Skewness 1.695866039
Sum 27168.66258
Variance 0.0948617973
Monotocity Not monotonic
2020-09-16T21:32:56.087072 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)  
0 46055 27.2%
 
0.917 192 0.1%
 
0.916 190 0.1%
 
0.913 186 0.1%
 
0.904 179 0.1%
 
0.901 177 0.1%
 
0.922 173 0.1%
 
0.894 171 0.1%
 
0.914 171 0.1%
 
0.903 170 0.1%
 
Other values (5391) 121638 71.8%
 
Value Count Frequency (%)  
0 46055 27.2%
 
1e-06 28 < 0.1%
 
1.01e-06 67 < 0.1%
 
1.02e-06 89 0.1%
 
1.03e-06 70 < 0.1%
 
Value Count Frequency (%)  
1 10 < 0.1%
 
0.999 13 < 0.1%
 
0.998 10 < 0.1%
 
0.997 3 < 0.1%
 
0.996 6 < 0.1%
 

loudness
Real number (ℝ)

Distinct 25313
Distinct (%) 15.0%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean -11.34211645
Minimum -60
Maximum 3.855
Zeros 0
Zeros (%) 0.0%
Memory size 1.3 MiB
2020-09-16T21:32:56.234469 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum -60
5-th percentile -21.80895
Q1 -14.42975
median -10.453
Q3 -7.109
95-th percentile -4.09305
Maximum 3.855
Range 63.855
Interquartile range (IQR) 7.32075

Descriptive statistics

Standard deviation 5.642668316
Coefficient of variation (CV) -0.4974969476
Kurtosis 1.932477728
Mean -11.34211645
Median Absolute Deviation (MAD) 3.59
Skewness -1.071872125
Sum -1920243
Variance 31.83970572
Monotocity Not monotonic
2020-09-16T21:32:56.369669 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)  
-7.006 27 < 0.1%
 
-7.436 27 < 0.1%
 
-7.632 26 < 0.1%
 
-9.298 26 < 0.1%
 
-8.32 26 < 0.1%
 
-6.942 26 < 0.1%
 
-11.815 25 < 0.1%
 
-7.566 25 < 0.1%
 
-11.451 25 < 0.1%
 
-8.789 25 < 0.1%
 
Other values (25303) 169044 99.8%
 
Value Count Frequency (%)  
-60 9 < 0.1%
 
-55 1 < 0.1%
 
-54.376 1 < 0.1%
 
-51.123 1 < 0.1%
 
-51.08 1 < 0.1%
 
Value Count Frequency (%)  
3.855 1 < 0.1%
 
3.744 1 < 0.1%
 
2.799 1 < 0.1%
 
1.963 1 < 0.1%
 
1.83 1 < 0.1%
 

speechiness
Real number (ℝ≥0)

Distinct 1628
Distinct (%) 1.0%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean 0.09419094281
Minimum 0
Maximum 0.969
Zeros 147
Zeros (%) 0.1%
Memory size 1.3 MiB
2020-09-16T21:32:56.516622 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum 0
5-th percentile 0.0281
Q1 0.0349
median 0.0451
Q3 0.0755
95-th percentile 0.332
Maximum 0.969
Range 0.969
Interquartile range (IQR) 0.0406

Descriptive statistics

Standard deviation 0.1501470001
Coefficient of variation (CV) 1.594070466
Kurtosis 19.30765722
Mean 0.09419094281
Median Absolute Deviation (MAD) 0.0131
Skewness 4.229128896
Sum 15946.715
Variance 0.02254412163
Monotocity Not monotonic
2020-09-16T21:32:56.660732 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)  
0.0347 571 0.3%
 
0.0319 563 0.3%
 
0.0333 558 0.3%
 
0.0337 557 0.3%
 
0.0334 555 0.3%
 
0.0352 554 0.3%
 
0.0332 551 0.3%
 
0.0315 549 0.3%
 
0.0336 548 0.3%
 
0.034 548 0.3%
 
Other values (1618) 163748 96.7%
 
Value Count Frequency (%)  
0 147 0.1%
 
0.0222 1 < 0.1%
 
0.0223 3 < 0.1%
 
0.0224 5 < 0.1%
 
0.0225 5 < 0.1%
 
Value Count Frequency (%)  
0.969 1 < 0.1%
 
0.968 3 < 0.1%
 
0.967 3 < 0.1%
 
0.966 17 < 0.1%
 
0.965 22 < 0.1%
 

tempo
Real number (ℝ≥0)

Distinct 84548
Distinct (%) 49.9%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean 116.9904707
Minimum 0
Maximum 244.091
Zeros 146
Zeros (%) 0.1%
Memory size 1.3 MiB
2020-09-16T21:32:56.825964 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum 0
5-th percentile 74.2402
Q1 93.5885
median 114.8315
Q3 135.7445
95-th percentile 174.4717
Maximum 244.091
Range 244.091
Interquartile range (IQR) 42.156

Descriptive statistics

Standard deviation 30.71802952
Coefficient of variation (CV) 0.2625686463
Kurtosis -0.07618982086
Mean 116.9904707
Median Absolute Deviation (MAD) 21.0875
Skewness 0.4485858734
Sum 19806720.67
Variance 943.5973373
Monotocity Not monotonic
2020-09-16T21:32:56.964333 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)  
0 146 0.1%
 
120 21 < 0.1%
 
119.989 18 < 0.1%
 
120.005 17 < 0.1%
 
119.994 17 < 0.1%
 
119.969 17 < 0.1%
 
120.012 17 < 0.1%
 
129.995 16 < 0.1%
 
120.011 16 < 0.1%
 
95.027 15 < 0.1%
 
Other values (84538) 169002 99.8%
 
Value Count Frequency (%)  
0 146 0.1%
 
30.946 1 < 0.1%
 
31.988 1 < 0.1%
 
32.466 1 < 0.1%
 
32.8 1 < 0.1%
 
Value Count Frequency (%)  
244.091 1 < 0.1%
 
243.507 1 < 0.1%
 
243.372 1 < 0.1%
 
238.895 1 < 0.1%
 
236.799 1 < 0.1%
 

valence
Real number (ℝ≥0)

Distinct 1739
Distinct (%) 1.0%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean 0.5329432862
Minimum 0
Maximum 1
Zeros 183
Zeros (%) 0.1%
Memory size 1.3 MiB
2020-09-16T21:32:57.107593 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum 0
5-th percentile 0.0938
Q1 0.324
median 0.545
Q3 0.75
95-th percentile 0.938
Maximum 1
Range 1
Interquartile range (IQR) 0.426

Descriptive statistics

Standard deviation 0.2621413115
Coefficient of variation (CV) 0.4918746859
Kurtosis -1.048579821
Mean 0.5329432862
Median Absolute Deviation (MAD) 0.213
Skewness -0.1267632558
Sum 90228.36424
Variance 0.0687180672
Monotocity Not monotonic
2020-09-16T21:32:57.247680 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)  
0.961 725 0.4%
 
0.962 597 0.4%
 
0.963 526 0.3%
 
0.964 467 0.3%
 
0.965 405 0.2%
 
0.96 388 0.2%
 
0.966 355 0.2%
 
0.967 311 0.2%
 
0.968 270 0.2%
 
0.559 252 0.1%
 
Other values (1729) 165006 97.5%
 
Value Count Frequency (%)  
0 183 0.1%
 
1e-05 75 < 0.1%
 
6.41e-05 1 < 0.1%
 
0.00049 1 < 0.1%
 
0.000537 1 < 0.1%
 
Value Count Frequency (%)  
1 3 < 0.1%
 
0.999 1 < 0.1%
 
0.998 1 < 0.1%
 
0.996 2 < 0.1%
 
0.995 1 < 0.1%
 

mode
Boolean

Distinct 2
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 1.3 MiB
1
119979 
0
49323 
Value Count Frequency (%)  
1 119979 70.9%
 
0 49323 29.1%
 
2020-09-16T21:32:57.340139 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/

key
Real number (ℝ≥0)

ZEROS

Distinct 12
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean 5.199761373
Minimum 0
Maximum 11
Zeros 21431
Zeros (%) 12.7%
Memory size 1.3 MiB
2020-09-16T21:32:57.409448 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum 0
5-th percentile 0
Q1 2
median 5
Q3 8
95-th percentile 11
Maximum 11
Range 11
Interquartile range (IQR) 6

Descriptive statistics

Standard deviation 3.51507068
Coefficient of variation (CV) 0.6760061524
Kurtosis -1.272421934
Mean 5.199761373
Median Absolute Deviation (MAD) 3
Skewness 0.004988843093
Sum 880330
Variance 12.35572188
Monotocity Not monotonic
2020-09-16T21:32:57.502266 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
Value Count Frequency (%)  
0 21431 12.7%
 
7 20697 12.2%
 
2 18760 11.1%
 
9 17580 10.4%
 
5 16279 9.6%
 
4 12881 7.6%
 
1 12775 7.5%
 
10 11984 7.1%
 
8 10672 6.3%
 
11 10552 6.2%
 
Other values (2) 15691 9.3%
 
Value Count Frequency (%)  
0 21431 12.7%
 
1 12775 7.5%
 
2 18760 11.1%
 
3 7139 4.2%
 
4 12881 7.6%
 
Value Count Frequency (%)  
11 10552 6.2%
 
10 11984 7.1%
 
9 17580 10.4%
 
8 10672 6.3%
 
7 20697 12.2%
 

popularity
Real number (ℝ≥0)

ZEROS

Distinct 100
Distinct (%) 0.1%
Missing 0
Missing (%) 0.0%
Infinite 0
Infinite (%) 0.0%
Mean 31.65795442
Minimum 0
Maximum 100
Zeros 26802
Zeros (%) 15.8%
Memory size 1.3 MiB
2020-09-16T21:32:57.622701 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum 0
5-th percentile 0
Q1 12
median 34
Q3 48
95-th percentile 65
Maximum 100
Range 100
Interquartile range (IQR) 36

Descriptive statistics

Standard deviation 21.54183438
Coefficient of variation (CV) 0.6804556636
Kurtosis -1.008177751
Mean 31.65795442
Median Absolute Deviation (MAD) 16
Skewness -0.02573882078
Sum 5359755
Variance 464.0506287
Monotocity Not monotonic
2020-09-16T21:32:57.764385 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Value Count Frequency (%)  
0 26802 15.8%
 
42 3280 1.9%
 
43 3120 1.8%
 
40 3061 1.8%
 
44 3053 1.8%
 
41 3016 1.8%
 
45 2944 1.7%
 
38 2900 1.7%
 
39 2868 1.7%
 
35 2858 1.7%
 
Other values (90) 115400 68.2%
 
Value Count Frequency (%)  
0 26802 15.8%
 
1 2248 1.3%
 
2 1448 0.9%
 
3 1200 0.7%
 
4 1074 0.6%
 
Value Count Frequency (%)  
100 1 < 0.1%
 
99 1 < 0.1%
 
97 1 < 0.1%
 
96 1 < 0.1%
 
95 4 < 0.1%
 

explicit
Boolean

Distinct 2
Distinct (%) < 0.1%
Missing 0
Missing (%) 0.0%
Memory size 1.3 MiB
0
154890 
1
 
14412
Value Count Frequency (%)  
0 154890 91.5%
 
1 14412 8.5%
 
2020-09-16T21:32:57.859175 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/

Interactions

2020-09-16T21:32:23.600489 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:23.760399 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:23.903188 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:24.051838 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:24.201073 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:24.349432 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:24.494856 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:24.642849 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:24.792094 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:24.940587 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:25.204828 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:25.352872 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:25.497574 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:25.641384 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:25.786618 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:25.923444 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:26.063677 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:26.204508 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:26.345129 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:26.484309 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:26.624627 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:26.765491 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:26.905866 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:27.046302 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:27.186557 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:27.323999 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:27.459798 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:27.606455 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:27.744289 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:27.884001 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:28.023800 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:28.165525 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:28.303280 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:28.443026 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:28.584565 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:28.728893 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:28.868767 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:29.007878 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:29.143902 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:29.280895 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:29.427141 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:29.565342 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:29.706805 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:29.846065 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:29.986716 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:30.242703 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:30.383688 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:30.525966 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:30.667846 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:30.807937 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:30.947932 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:31.084461 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:31.221347 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:31.367963 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:31.506655 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:31.647633 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:31.788014 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:31.927891 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:32.065296 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:32.206892 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:32.349449 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:32.490953 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:32.632069 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:32.772925 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:32.910271 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:33.046543 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:33.193090 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:33.332612 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:33.471390 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:33.610411 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:33.750659 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:33.886770 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:34.026507 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:34.168075 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:34.312907 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:34.456887 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:34.595328 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:34.731733 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:34.866826 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:35.014078 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:35.152969 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:35.293704 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:35.433457 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:35.579412 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:35.719231 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:35.859291 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:36.002225 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:36.141720 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:36.425711 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:36.565354 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:36.703159 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:36.839785 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:36.997082 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:37.148605 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:37.298964 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:37.441920 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:37.581109 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:37.720185 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:37.862791 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:38.007019 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:38.149979 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:38.292020 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:38.461148 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:38.621634 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:38.773105 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:38.949614 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:39.102745 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:39.247312 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:39.395386 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:39.595959 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:40.066278 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:40.284885 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:40.482327 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:40.699453 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:40.852272 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:41.058800 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:41.210802 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:41.351634 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:41.513145 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:41.664130 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:41.808025 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:41.951872 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:42.102296 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:42.243885 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:42.383506 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:42.524901 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:42.670851 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:42.817502 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:42.967177 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:43.108224 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:43.250059 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:43.402571 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:43.548793 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:43.689347 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:43.838058 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:43.987059 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:44.158782 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:44.396946 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:44.579710 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:44.720195 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:45.076459 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:45.222089 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:45.364448 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:45.515976 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:45.667412 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:45.818787 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:45.959657 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:46.109210 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:46.253717 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:46.391085 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:46.541688 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:46.691194 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:46.841018 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:46.989888 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:47.128411 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:47.263661 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:47.399865 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:47.546315 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:47.687341 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:47.827892 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:47.966915 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:48.111809 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:48.315902 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:48.621879 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:48.792752 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:48.952940 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:49.309397 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:49.621745 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:49.791870 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/

Correlations

2020-09-16T21:32:57.946834 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2020-09-16T21:32:58.180197 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2020-09-16T21:32:58.441702 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2020-09-16T21:32:58.712200 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2020-09-16T21:32:50.321632 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/
2020-09-16T21:32:50.989530 image/svg+xml Matplotlib v3.3.2, https://matplotlib.org/

Sample

First rows

df_index name artists duration_ms year acousticness danceability energy instrumentalness loudness speechiness tempo valence mode key popularity explicit
0 0 Singende Bataillone 1. Teil ['Carl Woitschach'] 158648 1928 0.995 0.708 0.1950 0.563 -12.428 0.0506 118.469 0.7790 1 10 0 0
1 1 Fantasiestücke, Op. 111: Più tosto lento ['Robert Schumann', 'Vladimir Horowitz'] 282133 1928 0.994 0.379 0.0135 0.901 -28.454 0.0462 83.972 0.0767 1 8 0 0
2 2 Chapter 1.18 - Zamek kaniowski ['Seweryn Goszczyński'] 104300 1928 0.604 0.749 0.2200 0.000 -19.924 0.9290 107.177 0.8800 0 5 0 0
3 3 Bebamos Juntos - Instrumental (Remasterizado) ['Francisco Canaro'] 180760 1928 0.995 0.781 0.1300 0.887 -14.734 0.0926 108.003 0.7200 0 1 0 0
4 4 Polonaise-Fantaisie in A-Flat Major, Op. 61 ['Frédéric Chopin', 'Vladimir Horowitz'] 687733 1928 0.990 0.210 0.2040 0.908 -16.829 0.0424 62.149 0.0693 1 11 1 0
5 5 Scherzo a capriccio: Presto ['Felix Mendelssohn', 'Vladimir Horowitz'] 352600 1928 0.995 0.424 0.1200 0.911 -19.242 0.0593 63.521 0.2660 0 6 0 0
6 6 Valse oubliée No. 1 in F-Sharp Major, S. 215/1 ['Franz Liszt', 'Vladimir Horowitz'] 136627 1928 0.956 0.444 0.1970 0.435 -17.226 0.0400 80.495 0.3050 1 11 0 0
7 7 Per aspera ad astra ['Carl Woitschach'] 153967 1928 0.988 0.555 0.4210 0.836 -9.878 0.0474 123.310 0.8570 1 1 0 0
8 8 Moneda Corriente - Remasterizado ['Francisco Canaro', 'Charlo'] 162493 1928 0.995 0.683 0.2070 0.206 -9.801 0.1270 119.833 0.4930 0 9 0 0
9 9 Chapter 1.3 - Zamek kaniowski ['Seweryn Goszczyński'] 111600 1928 0.846 0.674 0.2050 0.000 -20.119 0.9540 81.249 0.7590 1 9 0 0

Last rows

df_index name artists duration_ms year acousticness danceability energy instrumentalness loudness speechiness tempo valence mode key popularity explicit
169292 169899 Rough Ryder ['YoungBoy Never Broke Again'] 161019 2020 0.3710 0.623 0.721 0.000000 -4.584 0.3390 166.637 0.719 0 10 64 1
169293 169900 I Dare You ['Kelly Clarkson'] 216107 2020 0.0452 0.655 0.719 0.000018 -7.400 0.0368 124.034 0.435 1 2 69 0
169294 169901 Letter To Nipsey (feat. Roddy Ricch) ['Meek Mill', 'Roddy Ricch'] 167845 2020 0.2640 0.744 0.702 0.000000 -6.255 0.2880 91.885 0.338 0 7 66 1
169295 169902 Back Home (feat. Summer Walker) ['Trey Songz', 'Summer Walker'] 194576 2020 0.0227 0.619 0.719 0.000000 -4.111 0.1570 86.036 0.351 1 0 69 1
169296 169903 Ojos De Maniaco ['LEGADO 7', 'Junior H'] 218501 2020 0.2100 0.795 0.585 0.000001 -4.451 0.0374 97.479 0.934 1 8 68 0
169297 169904 Skechers (feat. Tyga) - Remix ['DripReport', 'Tyga'] 163800 2020 0.1730 0.875 0.443 0.000032 -7.461 0.1430 100.012 0.306 1 1 75 1
169298 169905 Sweeter (feat. Terrace Martin) ['Leon Bridges', 'Terrace Martin'] 167468 2020 0.0167 0.719 0.385 0.031300 -10.907 0.0403 128.000 0.270 1 8 64 0
169299 169906 How Would I Know ['Kygo', 'Oh Wonder'] 180700 2020 0.5380 0.514 0.539 0.002330 -9.332 0.1050 123.700 0.153 1 7 70 0
169300 169907 I Found You ['Cash Cash', 'Andy Grammer'] 167308 2020 0.0714 0.646 0.761 0.000000 -2.557 0.0385 129.916 0.472 1 1 70 0
169301 169908 More Hearts Than Mine ['Ingrid Andress'] 214787 2020 0.1090 0.512 0.428 0.000000 -7.387 0.0271 80.588 0.366 1 0 65 0